Overview

Dataset statistics

Number of variables12
Number of observations190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.9 KiB
Average record size in memory96.7 B

Variable types

Categorical2
Numeric10

Alerts

Impressions is highly correlated with Ad Group and 6 other fieldsHigh correlation
Clicks is highly correlated with Ad Group and 6 other fieldsHigh correlation
CTR is highly correlated with Ad GroupHigh correlation
Conversions is highly correlated with Impressions and 5 other fieldsHigh correlation
Conv Rate is highly correlated with Ad GroupHigh correlation
Cost is highly correlated with Ad Group and 6 other fieldsHigh correlation
CPC is highly correlated with Ad GroupHigh correlation
Revenue is highly correlated with Ad Group and 6 other fieldsHigh correlation
Sale Amount is highly correlated with Ad Group and 6 other fieldsHigh correlation
P&L is highly correlated with Impressions and 5 other fieldsHigh correlation
Ad Group is highly correlated with Impressions and 7 other fieldsHigh correlation
Ad Group is uniformly distributed Uniform
Month is uniformly distributed Uniform
Conversions has 7 (3.7%) zeros Zeros
Conv Rate has 7 (3.7%) zeros Zeros
Revenue has 8 (4.2%) zeros Zeros
Sale Amount has 7 (3.7%) zeros Zeros

Reproduction

Analysis started2022-10-16 06:05:45.986982
Analysis finished2022-10-16 06:06:16.119965
Duration30.13 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Ad Group
Categorical

HIGH CORRELATION
UNIFORM

Distinct40
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Shop - 1:1 - Desk - [shop coupon code]
 
5
Shop - Phrase - Desk - Promo Code
 
5
Shop - Exact - Mob - Offer
 
5
Shop - Exact - Mob - Promo Code
 
5
Shop - Exact - Mob - Sale
 
5
Other values (35)
165 

Length

Max length47
Median length38
Mean length32.64210526
Min length25

Characters and Unicode

Total characters6202
Distinct characters37
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.1%

Sample

1st rowShop - 1:1 - Desk - [shop coupon code]
2nd rowShop - 1:1 - Desk - [shop coupon]
3rd rowShop - 1:1 - Desk - [shop discount code]
4th rowShop - 1:1 - Desk - [shop promo code]
5th rowShop - 1:1 - Desk - [shop promo]

Common Values

ValueCountFrequency (%)
Shop - 1:1 - Desk - [shop coupon code]5
 
2.6%
Shop - Phrase - Desk - Promo Code5
 
2.6%
Shop - Exact - Mob - Offer5
 
2.6%
Shop - Exact - Mob - Promo Code5
 
2.6%
Shop - Exact - Mob - Sale5
 
2.6%
Shop - Phrase - Desk - Coupon Code5
 
2.6%
Shop - Phrase - Desk - Discount Code5
 
2.6%
Shop - Phrase - Desk - Free Shipping5
 
2.6%
Shop - Phrase - Desk - Offer5
 
2.6%
Shop - Phrase - Desk - Sale5
 
2.6%
Other values (30)140
73.7%

Length

2022-10-16T11:36:16.363866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
570
38.2%
shop240
16.1%
mob96
 
6.4%
desk94
 
6.3%
code90
 
6.0%
exact72
 
4.8%
phrase68
 
4.6%
1:150
 
3.3%
coupon40
 
2.7%
promo40
 
2.7%
Other values (9)134
 
9.0%

Most occurring characters

ValueCountFrequency (%)
1304
21.0%
o654
 
10.5%
-570
 
9.2%
p358
 
5.8%
e352
 
5.7%
h328
 
5.3%
s242
 
3.9%
S230
 
3.7%
r170
 
2.7%
a166
 
2.7%
Other values (27)1828
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3330
53.7%
Space Separator1304
 
21.0%
Uppercase Letter746
 
12.0%
Dash Punctuation570
 
9.2%
Decimal Number100
 
1.6%
Other Punctuation52
 
0.8%
Close Punctuation50
 
0.8%
Open Punctuation50
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o654
19.6%
p358
10.8%
e352
10.6%
h328
9.8%
s242
 
7.3%
r170
 
5.1%
a166
 
5.0%
c154
 
4.6%
t138
 
4.1%
d104
 
3.1%
Other values (11)664
19.9%
Uppercase Letter
ValueCountFrequency (%)
S230
30.8%
D114
15.3%
C100
13.4%
M98
13.1%
P88
 
11.8%
E72
 
9.7%
F22
 
2.9%
O20
 
2.7%
B2
 
0.3%
Other Punctuation
ValueCountFrequency (%)
:50
96.2%
/2
 
3.8%
Space Separator
ValueCountFrequency (%)
1304
100.0%
Dash Punctuation
ValueCountFrequency (%)
-570
100.0%
Decimal Number
ValueCountFrequency (%)
1100
100.0%
Close Punctuation
ValueCountFrequency (%)
]50
100.0%
Open Punctuation
ValueCountFrequency (%)
[50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4076
65.7%
Common2126
34.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o654
16.0%
p358
 
8.8%
e352
 
8.6%
h328
 
8.0%
s242
 
5.9%
S230
 
5.6%
r170
 
4.2%
a166
 
4.1%
c154
 
3.8%
t138
 
3.4%
Other values (20)1284
31.5%
Common
ValueCountFrequency (%)
1304
61.3%
-570
26.8%
1100
 
4.7%
]50
 
2.4%
[50
 
2.4%
:50
 
2.4%
/2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1304
21.0%
o654
 
10.5%
-570
 
9.2%
p358
 
5.8%
e352
 
5.7%
h328
 
5.3%
s242
 
3.9%
S230
 
3.7%
r170
 
2.7%
a166
 
2.7%
Other values (27)1828
29.5%

Month
Categorical

UNIFORM

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
November
39 
July
38 
August
38 
September
38 
October
37 

Length

Max length9
Median length7
Mean length6.805263158
Min length4

Characters and Unicode

Total characters1293
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
November39
20.5%
July38
20.0%
August38
20.0%
September38
20.0%
October37
19.5%

Length

2022-10-16T11:36:16.674597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-16T11:36:16.938611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
november39
20.5%
july38
20.0%
august38
20.0%
september38
20.0%
october37
19.5%

Most occurring characters

ValueCountFrequency (%)
e229
17.7%
b114
 
8.8%
r114
 
8.8%
u114
 
8.8%
t113
 
8.7%
m77
 
6.0%
o76
 
5.9%
N39
 
3.0%
v39
 
3.0%
s38
 
2.9%
Other values (9)340
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1103
85.3%
Uppercase Letter190
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e229
20.8%
b114
10.3%
r114
10.3%
u114
10.3%
t113
10.2%
m77
 
7.0%
o76
 
6.9%
v39
 
3.5%
s38
 
3.4%
p38
 
3.4%
Other values (4)151
13.7%
Uppercase Letter
ValueCountFrequency (%)
N39
20.5%
S38
20.0%
A38
20.0%
J38
20.0%
O37
19.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1293
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e229
17.7%
b114
 
8.8%
r114
 
8.8%
u114
 
8.8%
t113
 
8.7%
m77
 
6.0%
o76
 
5.9%
N39
 
3.0%
v39
 
3.0%
s38
 
2.9%
Other values (9)340
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e229
17.7%
b114
 
8.8%
r114
 
8.8%
u114
 
8.8%
t113
 
8.7%
m77
 
6.0%
o76
 
5.9%
N39
 
3.0%
v39
 
3.0%
s38
 
2.9%
Other values (9)340
26.3%

Impressions
Real number (ℝ≥0)

HIGH CORRELATION

Distinct189
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14077.36316
Minimum35
Maximum276568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-10-16T11:36:17.165638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile69.25
Q11065
median4969
Q313380
95-th percentile56080.6
Maximum276568
Range276533
Interquartile range (IQR)12315

Descriptive statistics

Standard deviation29771.68623
Coefficient of variation (CV)2.114862414
Kurtosis35.80165748
Mean14077.36316
Median Absolute Deviation (MAD)4302
Skewness5.173273236
Sum2674699
Variance886353300.8
MonotonicityNot monotonic
2022-10-16T11:36:17.418845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32792
 
1.1%
17701
 
0.5%
62541
 
0.5%
69081
 
0.5%
157691
 
0.5%
50441
 
0.5%
57891
 
0.5%
96191
 
0.5%
23361
 
0.5%
47151
 
0.5%
Other values (179)179
94.2%
ValueCountFrequency (%)
351
0.5%
361
0.5%
381
0.5%
441
0.5%
501
0.5%
521
0.5%
541
0.5%
551
0.5%
661
0.5%
671
0.5%
ValueCountFrequency (%)
2765681
0.5%
1523941
0.5%
1388111
0.5%
1059661
0.5%
992581
0.5%
934061
0.5%
908061
0.5%
734481
0.5%
640671
0.5%
573731
0.5%

Clicks
Real number (ℝ≥0)

HIGH CORRELATION

Distinct183
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4865.805263
Minimum2
Maximum99526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-10-16T11:36:17.665244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11.45
Q1264.5
median930
Q34190.5
95-th percentile22736.1
Maximum99526
Range99524
Interquartile range (IQR)3926

Descriptive statistics

Standard deviation11348.52922
Coefficient of variation (CV)2.332302385
Kurtosis30.71189062
Mean4865.805263
Median Absolute Deviation (MAD)826
Skewness4.888967218
Sum924503
Variance128789115.4
MonotonicityNot monotonic
2022-10-16T11:36:17.914328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93
 
1.6%
82
 
1.1%
5932
 
1.1%
1232
 
1.1%
3412
 
1.1%
1632
 
1.1%
65041
 
0.5%
25101
 
0.5%
29081
 
0.5%
8551
 
0.5%
Other values (173)173
91.1%
ValueCountFrequency (%)
21
 
0.5%
31
 
0.5%
41
 
0.5%
51
 
0.5%
82
1.1%
93
1.6%
111
 
0.5%
121
 
0.5%
131
 
0.5%
141
 
0.5%
ValueCountFrequency (%)
995261
0.5%
591771
0.5%
574051
0.5%
422831
0.5%
406221
0.5%
360681
0.5%
320051
0.5%
271211
0.5%
252831
0.5%
235381
0.5%

CTR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2721052632
Minimum0.05
Maximum0.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-10-16T11:36:18.304879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.09
Q10.1925
median0.285
Q30.36
95-th percentile0.43
Maximum0.47
Range0.42
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.1078936841
Coefficient of variation (CV)0.3965145063
Kurtosis-0.9383087548
Mean0.2721052632
Median Absolute Deviation (MAD)0.085
Skewness-0.3031002304
Sum51.7
Variance0.01164104706
MonotonicityNot monotonic
2022-10-16T11:36:18.541801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.3410
 
5.3%
0.379
 
4.7%
0.358
 
4.2%
0.247
 
3.7%
0.257
 
3.7%
0.097
 
3.7%
0.237
 
3.7%
0.397
 
3.7%
0.267
 
3.7%
0.37
 
3.7%
Other values (32)114
60.0%
ValueCountFrequency (%)
0.051
 
0.5%
0.061
 
0.5%
0.074
2.1%
0.083
1.6%
0.097
3.7%
0.12
 
1.1%
0.113
1.6%
0.124
2.1%
0.134
2.1%
0.145
2.6%
ValueCountFrequency (%)
0.472
 
1.1%
0.453
 
1.6%
0.443
 
1.6%
0.434
2.1%
0.421
 
0.5%
0.413
 
1.6%
0.47
3.7%
0.397
3.7%
0.385
2.6%
0.379
4.7%

Conversions
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct137
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.2421053
Minimum0
Maximum7563
Zeros7
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-10-16T11:36:18.789174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q124
median70.5
Q3428.25
95-th percentile2248.55
Maximum7563
Range7563
Interquartile range (IQR)404.25

Descriptive statistics

Standard deviation1052.202922
Coefficient of variation (CV)2.082571723
Kurtosis17.79051328
Mean505.2421053
Median Absolute Deviation (MAD)68.5
Skewness3.81395678
Sum95996
Variance1107130.989
MonotonicityNot monotonic
2022-10-16T11:36:19.035166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07
 
3.7%
25
 
2.6%
245
 
2.6%
14
 
2.1%
254
 
2.1%
144
 
2.1%
133
 
1.6%
33
 
1.6%
263
 
1.6%
613
 
1.6%
Other values (127)149
78.4%
ValueCountFrequency (%)
07
3.7%
14
2.1%
25
2.6%
33
1.6%
41
 
0.5%
72
 
1.1%
91
 
0.5%
101
 
0.5%
111
 
0.5%
121
 
0.5%
ValueCountFrequency (%)
75631
0.5%
59611
0.5%
57821
0.5%
43491
0.5%
40801
0.5%
29401
0.5%
27131
0.5%
26661
0.5%
24441
0.5%
22941
0.5%

Conv Rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07973684211
Minimum0
Maximum0.5
Zeros7
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-10-16T11:36:19.248527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.05
median0.07
Q30.1
95-th percentile0.14
Maximum0.5
Range0.5
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.05285934426
Coefficient of variation (CV)0.6629224693
Kurtosis23.45682501
Mean0.07973684211
Median Absolute Deviation (MAD)0.02
Skewness3.592692129
Sum15.15
Variance0.002794110276
MonotonicityNot monotonic
2022-10-16T11:36:19.481435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0527
14.2%
0.0722
11.6%
0.0619
10.0%
0.0419
10.0%
0.0917
8.9%
0.116
8.4%
0.0814
7.4%
0.1112
6.3%
0.1212
6.3%
0.038
 
4.2%
Other values (11)24
12.6%
ValueCountFrequency (%)
07
 
3.7%
0.011
 
0.5%
0.038
 
4.2%
0.0419
10.0%
0.0527
14.2%
0.0619
10.0%
0.0722
11.6%
0.0814
7.4%
0.0917
8.9%
0.116
8.4%
ValueCountFrequency (%)
0.51
 
0.5%
0.331
 
0.5%
0.281
 
0.5%
0.211
 
0.5%
0.191
 
0.5%
0.172
 
1.1%
0.152
 
1.1%
0.144
 
2.1%
0.133
 
1.6%
0.1212
6.3%

Cost
Real number (ℝ≥0)

HIGH CORRELATION

Distinct175
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3344.063158
Minimum1
Maximum43542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-10-16T11:36:19.734706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q1188.5
median563.5
Q32967
95-th percentile13223.55
Maximum43542
Range43541
Interquartile range (IQR)2778.5

Descriptive statistics

Standard deviation6524.606753
Coefficient of variation (CV)1.951101533
Kurtosis15.8262848
Mean3344.063158
Median Absolute Deviation (MAD)557
Skewness3.615090827
Sum635372
Variance42570493.29
MonotonicityNot monotonic
2022-10-16T11:36:19.978165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64
 
2.1%
53
 
1.6%
13
 
1.6%
33
 
1.6%
522
 
1.1%
1112
 
1.1%
82
 
1.1%
1342
 
1.1%
11182
 
1.1%
2722
 
1.1%
Other values (165)165
86.8%
ValueCountFrequency (%)
13
1.6%
21
 
0.5%
33
1.6%
41
 
0.5%
53
1.6%
64
2.1%
71
 
0.5%
82
1.1%
181
 
0.5%
351
 
0.5%
ValueCountFrequency (%)
435421
0.5%
382731
0.5%
377291
0.5%
273361
0.5%
241491
0.5%
193711
0.5%
186411
0.5%
169461
0.5%
137461
0.5%
132781
0.5%

CPC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7912631579
Minimum0.14
Maximum2.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-10-16T11:36:20.204670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.14
5-th percentile0.279
Q10.48
median0.635
Q31.1075
95-th percentile1.4755
Maximum2.02
Range1.88
Interquartile range (IQR)0.6275

Descriptive statistics

Standard deviation0.403312323
Coefficient of variation (CV)0.50970694
Kurtosis-0.4407450849
Mean0.7912631579
Median Absolute Deviation (MAD)0.25
Skewness0.6533200925
Sum150.34
Variance0.1626608299
MonotonicityNot monotonic
2022-10-16T11:36:20.450059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.595
 
2.6%
0.575
 
2.6%
0.495
 
2.6%
0.625
 
2.6%
0.524
 
2.1%
0.514
 
2.1%
0.414
 
2.1%
0.484
 
2.1%
0.444
 
2.1%
0.384
 
2.1%
Other values (90)146
76.8%
ValueCountFrequency (%)
0.141
0.5%
0.171
0.5%
0.191
0.5%
0.222
1.1%
0.232
1.1%
0.251
0.5%
0.261
0.5%
0.271
0.5%
0.291
0.5%
0.31
0.5%
ValueCountFrequency (%)
2.021
0.5%
1.921
0.5%
1.871
0.5%
1.621
0.5%
1.61
0.5%
1.551
0.5%
1.511
0.5%
1.492
1.1%
1.481
0.5%
1.471
0.5%

Revenue
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct172
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2957.684211
Minimum0
Maximum42440
Zeros8
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-10-16T11:36:20.702420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.35
Q1144.5
median459.5
Q32672.25
95-th percentile13163
Maximum42440
Range42440
Interquartile range (IQR)2527.75

Descriptive statistics

Standard deviation5962.413097
Coefficient of variation (CV)2.015905916
Kurtosis17.41256614
Mean2957.684211
Median Absolute Deviation (MAD)451
Skewness3.761989652
Sum561960
Variance35550369.94
MonotonicityNot monotonic
2022-10-16T11:36:20.948341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
4.2%
1733
 
1.6%
1652
 
1.1%
2022
 
1.1%
222
 
1.1%
72
 
1.1%
762
 
1.1%
2952
 
1.1%
672
 
1.1%
1622
 
1.1%
Other values (162)163
85.8%
ValueCountFrequency (%)
08
4.2%
11
 
0.5%
41
 
0.5%
72
 
1.1%
82
 
1.1%
121
 
0.5%
131
 
0.5%
141
 
0.5%
161
 
0.5%
201
 
0.5%
ValueCountFrequency (%)
424401
0.5%
345181
0.5%
326681
0.5%
240711
0.5%
238571
0.5%
165551
0.5%
145651
0.5%
137441
0.5%
136991
0.5%
132621
0.5%

Sale Amount
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct184
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63416.18058
Minimum0
Maximum886095.31
Zeros7
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2022-10-16T11:36:21.177763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile108.7105
Q12985.135
median10274.105
Q359345.195
95-th percentile284099.7585
Maximum886095.31
Range886095.31
Interquartile range (IQR)56360.06

Descriptive statistics

Standard deviation125414.6569
Coefficient of variation (CV)1.97764444
Kurtosis16.69701991
Mean63416.18058
Median Absolute Deviation (MAD)10072.2
Skewness3.674198041
Sum12049074.31
Variance1.572883617 × 1010
MonotonicityNot monotonic
2022-10-16T11:36:21.418641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07
 
3.7%
136770.051
 
0.5%
8585.471
 
0.5%
55815.231
 
0.5%
34980.161
 
0.5%
40122.761
 
0.5%
25311.151
 
0.5%
25104.241
 
0.5%
12245.371
 
0.5%
9859.21
 
0.5%
Other values (174)174
91.6%
ValueCountFrequency (%)
07
3.7%
5.961
 
0.5%
28.931
 
0.5%
83.21
 
0.5%
139.891
 
0.5%
147.751
 
0.5%
157.971
 
0.5%
168.541
 
0.5%
235.271
 
0.5%
261.41
 
0.5%
ValueCountFrequency (%)
886095.311
0.5%
725773.921
0.5%
677188.11
0.5%
500400.851
0.5%
497790.811
0.5%
345891.361
0.5%
321387.881
0.5%
294536.921
0.5%
285638.31
0.5%
284823.481
0.5%

P&L
Real number (ℝ)

HIGH CORRELATION

Distinct188
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-386.3619158
Minimum-5672.271
Maximum1507.685
Zeros0
Zeros (%)0.0%
Negative151
Negative (%)79.5%
Memory size1.6 KiB
2022-10-16T11:36:21.864069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-5672.271
5-th percentile-2028.99835
Q1-294.9775
median-75.978
Q3-6.0275
95-th percentile116.414
Maximum1507.685
Range7179.956
Interquartile range (IQR)288.95

Descriptive statistics

Standard deviation903.0737755
Coefficient of variation (CV)-2.337377828
Kurtosis14.51111736
Mean-386.3619158
Median Absolute Deviation (MAD)88.861
Skewness-3.389103267
Sum-73408.764
Variance815542.244
MonotonicityNot monotonic
2022-10-16T11:36:22.145137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12
 
1.1%
-62
 
1.1%
-267.0861
 
0.5%
168.3731
 
0.5%
337.1251
 
0.5%
-79.5951
 
0.5%
342.9791
 
0.5%
-265.4241
 
0.5%
97.7641
 
0.5%
-27.8541
 
0.5%
Other values (178)178
93.7%
ValueCountFrequency (%)
-5672.2711
0.5%
-5605.2971
0.5%
-4606.7131
0.5%
-3478.6441
0.5%
-3211.1211
0.5%
-3012.911
0.5%
-2380.6161
0.5%
-2240.0761
0.5%
-2170.0441
0.5%
-2086.31
0.5%
ValueCountFrequency (%)
1507.6851
0.5%
766.8591
0.5%
762.9211
0.5%
342.9791
0.5%
337.1251
0.5%
308.9751
0.5%
181.5971
0.5%
168.3731
0.5%
117.1361
0.5%
116.7831
0.5%

Interactions

2022-10-16T11:36:13.497683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:53.547911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:56.064548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:58.135761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:00.355064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:02.595060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:04.843932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:07.017117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:09.249861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:11.416698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:13.705530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:53.900909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:56.272033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:58.336261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:00.665196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:02.819021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:05.049016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:07.220233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:09.455418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:11.624518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:14.052946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:54.209204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:56.476522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:58.683887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:00.885711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:03.019526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:05.247049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:07.425268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:09.658819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:11.826120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:14.257432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:54.443610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:56.675028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:58.886278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:01.124780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:03.222423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:05.443295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:07.626746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:09.866301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:12.030614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:14.474891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:54.692516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:56.890049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:59.099291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:01.342231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:03.435781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:05.661742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:07.849180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:10.078796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:12.246087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:14.684041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:54.935886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:57.096532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:59.309755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:01.549716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:03.781417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:05.971482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:08.060273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:10.290271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:12.459549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:14.887564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:55.210717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:57.306013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:59.509260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:01.763190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:03.990022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:06.178000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:08.271302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:10.486977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:12.662050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:15.107255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:55.456088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:57.518483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:59.720067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:01.973682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:04.196501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:06.403608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:08.476828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:10.689058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:12.867216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:15.318667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:55.656080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:57.723967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:59.930541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:02.168130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:04.412949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:06.607606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:08.823935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:10.894516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:13.068758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:15.524671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:55.860059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:35:57.923296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:00.135618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:02.380092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:04.629469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:06.810092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:09.036396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:11.158303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T11:36:13.272245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-16T11:36:22.364681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-16T11:36:22.631003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-16T11:36:22.888864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-16T11:36:23.139745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-16T11:36:23.366500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-16T11:36:15.801471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-16T11:36:16.062145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Ad GroupMonthImpressionsClicksCTRConversionsConv RateCostCPCRevenueSale AmountP&L
0Shop - 1:1 - Desk - [shop coupon code]July1603865040.4111660.1066691.036402136770.05-267.086
1Shop - 1:1 - Desk - [shop coupon]July36462143670.3921880.09137460.9613262283215.21-483.951
2Shop - 1:1 - Desk - [shop discount code]July363514580.402480.0916061.10172339165.46117.136
3Shop - 1:1 - Desk - [shop promo code]July26185104180.4022940.12132781.2713042284823.48-235.921
4Shop - 1:1 - Desk - [shop promo]July8082820.35610.153911.393377717.77-53.604
5Shop - 1:1 - Mob - [shop coupon code]July46507217560.4716650.05131570.608550185824.49-4606.713
6Shop - 1:1 - Mob - [shop coupon]July152394591770.3926660.04193710.3313699294536.92-5672.271
7Shop - 1:1 - Mob - [shop discount code]July995042830.433470.0526370.62203846026.32-598.993
8Shop - 1:1 - Mob - [shop promo code]July57373271210.4729400.07169460.6214565321387.88-2380.616
9Shop - 1:1 - Mob - [shop promo]July18757060.38690.074850.694098946.99-75.734

Last rows

Ad GroupMonthImpressionsClicksCTRConversionsConv RateCostCPCRevenueSale AmountP&L
180Shop - Phrase - Mob - CompetitorNovember127320.2520.04180.597139.89-11.005
181Shop - Phrase - Desk - Free ShippingNovember8590.1100.0020.3200.00-2.000
182Shop - Phrase - Mob - Free ShippingNovember209310.1520.0570.237147.750.389
183Shop - 1:1 - Mob - [shop promo]November582819750.342140.0911180.57112122936.403.104
184Shop - Exact - Mob - OfferNovember683812350.18850.076410.5267614020.8634.512
185Shop - Exact - Desk - Black Friday/Cyber MondayNovember257240.0970.2830.1445898.8041.946
186Shop - 1:1 - Desk - [shop discount code]November725427250.385120.1131821.17322766672.2945.468
187Shop - Exact - Desk - Coupon CodeNovember1852655530.309190.1059821.086047129556.9064.552
188Shop - Exact - Mob - Black Friday/Cyber MondayNovember36622660.07240.09440.171603268.63115.963
189Shop - Exact - Desk - Promo CodeNovember2559277260.3017310.14109141.4111223236665.59308.975